title: Bayesian inference of population size history from multiple loci.
abstract: BACKGROUND
  Effective population size (Ne) is related to genetic variability and is a basic parameter in many models of population genetics. A number of methods for inferring current and past population sizes from genetic data have been developed since JFC Kingman introduced the n-coalescent in 1982. Here we present the Extended Bayesian Skyline Plot, a non-parametric Bayesian Markov chain Monte Carlo algorithm that extends a previous coalescent-based method in several ways, including the ability to analyze multiple loci.
  
  
  RESULTS
  Through extensive simulations we show the accuracy and limitations of inferring population size as a function of the amount of data, including recovering information about evolutionary bottlenecks. We also analyzed two real data sets to demonstrate the behavior of the new method; a single gene Hepatitis C virus data set sampled from Egypt and a 10 locus Drosophila ananassae data set representing 16 different populations.
  
  
  CONCLUSIONS
  The results demonstrate the essential role of multiple loci in recovering population size dynamics. Multi-locus data from a small number of individuals can precisely recover past bottlenecks in population size which can not be characterized by analysis of a single locus. We also demonstrate that sequence data quality is important because even moderate levels of sequencing errors result in a considerable decrease in estimation accuracy for realistic levels of population genetic variability.
authors.affiliation: Department of Computer Science, University of Auckland, Auckland, New Zealand. jheled@gmail.com
authors.forename: Joseph
authors.initials: J
authors.lastname: Heled
authors.forename: Alexei J
authors.initials: AJ
authors.lastname: Drummond
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D004330:Drosophila / Q000235:genetics*; D016174:Hepacivirus / Q000235:genetics*; D008957:Models, Genetic; D015233:Models, Statistical